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News Article | August 2, 2017
Site: www.prnewswire.com

This partnership offers MPF Program participating financial institutions, or PFIs, an expanded landscape for selling the servicing on their MPF traditional loans. CMC Funding offers market competitive pricing that can be passed down to borrowers. Another advantage is the capability to upload servicing files using the MPF Program's online transactional website, eMPF®. CMC Funding strives to make the delivery process smooth by customizing delivery processes for its mortgage lenders. "Our goal is for MPF Program participants who sell servicing to CMC Funding to realize they've gained a trusted partner for their mortgage business," said Tom Millon, President and CEO at CMC Funding. "We are excited about our partnership with CMC Funding as it fuels our ongoing mission to support community lenders nationwide," said John Stocchetti, Executive Vice President & Group Head, MPF Program at the Federal Home Loan Bank of Chicago. "The FHLBanks are here to make the job of the local lender easier by, among other things, providing the tools and services they need to better serve the borrowers in their communities." For further information and get signed up to begin using the CMC Funding servicing released option, MPF Program participants should contact their Federal Home Loan Bank. About Capital Markets Cooperative Capital Markets Cooperative (CMC), a Computershare company, leverages the collective power of a nationwide network of mortgage bankers to negotiate better products, services, pricing, and liquidity solutions during the processing, sale and servicing of mortgages. Learn more at: https://www.capmkts.org. About the MPF Program Learn more at www.fhlbmpf.com. "Mortgage Partnership Finance" and "MPF" are registered trademarks of the Federal Home Loan Bank of Chicago. The "MPF Mortgage Partnership Finance" logo is a trademark of the Federal Home Loan Bank of Chicago.


Cao L.,University of Technology, Sydney | Cao L.,Cooperative Capital | Zhang H.,Centrelink | Zhao Y.,Centrelink | And 2 more authors.
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | Year: 2011

Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informative knowledge. It would also be very time and space consuming, if not impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, catering for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more informative patterns, e.g., integrating frequent pattern mining with classifications to generate frequent pattern-based classifiers. Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on demand. We summarize general frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Novel types of combined patterns, such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been conducted to test the frameworks, with some of them briefed in this paper. They identify combined patterns for informing government debt prevention and improving government service objectives, which show the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data. © 2010 IEEE.


Ireland K.B.,Cooperative Capital | Ireland K.B.,Murdoch University | Hardy G.E.S.J.,Cooperative Capital | Hardy G.E.S.J.,Murdoch University | And 2 more authors.
PLoS ONE | Year: 2013

Phytophthora ramorum, an invasive plant pathogen of unknown origin, causes considerable and widespread damage in plant industries and natural ecosystems of the USA and Europe. Estimating the potential geographical range of P. ramorum has been complicated by a lack of biological and geographical data with which to calibrate climatic models. Previous attempts to do so, using either invaded range data or surrogate species approaches, have delivered varying results. A simulation model was developed using CLIMEX to estimate the global climate suitability patterns for establishment of P. ramorum. Growth requirements and stress response parameters were derived from ecophysiological laboratory observations and site-level transmission and disease factors related to climate data in the field. Geographical distribution data from the USA (California and Oregon) and Norway were reserved from model-fitting and used to validate the models. The model suggests that the invasion of P. ramorum in both North America and Europe is still in its infancy and that it is presently occupying a small fraction of its potential range. Phytophthora ramorum appears to be climatically suited to large areas of Africa, Australasia and South America, where it could cause biodiversity and economic losses in plant industries and natural ecosystems with susceptible hosts if introduced. © 2013 Ireland et al.


PubMed | Cooperative Capital, DNQB Plant Quarantine International Airport Nicolau Lobato Comoro, University of Western Australia and Ministry of Agriculture, Fisheries and Food
Type: Journal Article | Journal: Genome announcements | Year: 2017

We present here the first complete genomic Aphid lethal paralysis virus (ALPV) sequence isolated from cucumber plant RNA from East Timor. We compare it with two complete ALPV genome sequences from China, and one each from Israel, South Africa, and the United States. It most closely resembled the Chinese isolate LGH genome.


PubMed | Capital Medical University, Cooperative Capital, Southern Medical University and Harbin Medical University
Type: | Journal: Seizure | Year: 2016

Epilepsy is one of the most common manifestations in gliomas and has a severe effect on the life expectancy and quality of life of patients. The aim of our study was to assess the potential connections between clinicopathological factors and postoperative seizure.We retrospectively investigated a group of 147 Chinese high-grade glioma (HGG) patients with preoperative seizure to examine the correlation between postoperative seizure and clinicopathological factors and prognosis. Univariate analyses and multivariate logistic regression analyses were performed to identify factors associated with postoperative seizures. Survival function curves were calculated using the Kaplan-Meier method.53 patients (36%) were completely seizure-free (Engel class I), and 94 (64%) experienced a postoperative seizure (Engel classes II, III, and IV). A Chi-squared analysis showed that anaplastic oligodendroglioma/anaplastic oligoastrocytoma (AO/AOA) (P=0.05), epidermal growth factor receptor (EGFR) expression (P=0.0004), O(6)-methylguanine DNA methyltransferase (MGMT) expression (P=0.011), and phosphatase and tensin homolog (PTEN) expression (P=0.045) were all significantly different. A logistic regression analysis showed that MGMT expression (P=0.05), EGFR expression (P=0.001), and AO/AOA (P=0.038) are independent factors of postoperative seizure. Patients with lower MGMT and EGFR expression and AO/AOA showed more frequent instances of postoperative seizure. Postoperative seizure showed no statistical significance on overall survival (OS) and progression-free survival (PFS).Our study identified clinicopathological factors related to postoperative seizure in HGGs and found two predictive biomarkers of postoperative seizure: MGMT and EGFR. These findings provided insight treatment strategies aimed at prolonging survival and improving quality of life.


PubMed | CAS Institute of Biophysics, Beijing Neurosurgical Institute, Neurosurgery of Beijing Tiantan Hospital, Cooperative Capital and 2 more.
Type: | Journal: Clinical cancer research : an official journal of the American Association for Cancer Research | Year: 2016

Purpose RNA sequencing (RNA-seq) has recently proved to be effective for revealing novel virus-tumor associations. To get a thorough investigation of virus-glioma associations, we screened viruses in gliomas with RNA-seq data from Chinese Glioma Genome Atlas project (CGGA). Experimental Design In total, 325 samples were enrolled into this study. Reads that failed to map to human genome were aligned to viral genomes and screened for potential virus-derived transcripts. For quantification, VPKM was calculated according to mapped reads weighted by genome sizes and sequencing depth. Results We observed that viruses tended to concertedly express in a certain subgroup of patients. Survival analysis revealed that individuals who were infected with Simian Virus 40 (SV40) or Woolly Monkey Sarcoma Virus (WMSV) had a significant shorter overall survival than those uninfected. A multivariate Cox proportional hazards model, taking clinical and molecular factors into account, was applied to assess the prognostic value of SV40 and WMSV. Both SV40 and WMSV were independent prognostic factors for predicting patients survival in lower grade gliomas. Subsequent gene analysis demonstrated that SV40 was correlated with negative regulation of regulation and gene expression, while WMSV was correlated with cell cycle phase which indicated frequent proliferation of tumor cells. Conclusions Therefore, RNA sequencing was sufficient to identify virus infection in glioma samples. SV40 and WMSV were identified to be prognostic markers for lower grade glioma patients, and showed potential values for targeting therapy.


Srinivasan U.,Cooperative Capital | Arunasalam B.,Cooperative Capital
IT Professional | Year: 2013

The healthcare sector deals with large volumes of electronic data related to patient services. This article describes two novel applications that leverage big data to detect fraud, abuse, waste, and errors in health insurance claims, thus reducing recurrent losses and facilitating enhanced patient care. The results indicate that claim anomalies detected using these applications help private health insurance funds recover hidden cost overruns that aren't detectable using transaction processing systems. This article is part of a special issue on leveraging big data and business analytics. © 1999-2012 IEEE.


Uddin S.,University of Sydney | Kelaher M.,University of Melbourne | Srinivasan U.,Cooperative Capital
Australian Health Review | Year: 2016

Previous studies have documented the application of electronic health insurance claim data for health services research purposes. In addition to administrative and billing details of healthcare services, insurance data reveal important information regarding professional interactions and/or links that emerge among healthcare service providers through, for example, informal knowledge sharing. By using details of such professional interactions and social network analysis methods, the aim of the present study was to develop a research framework to explore health care coordination and collaboration. The proposed framework was used to analyse a patient-centric care coordination network and a physician collaboration network. The usefulness of this framework and its applications in exploring collaborative efforts of different healthcare professionals and service providers is discussed. What is known about the topic? Application of methods and measures of social network analytics in exploring different health care collaboration and coordination networks is a comparatively new research direction. It is apparent that no other study in the present healthcare literature proposes a generic framework for examining health care collaboration and coordination using an administrative claim dataset. What does this paper add? Using methods and measures of social network analytics, this paper proposes a generic framework for analysing various health care collaboration and coordination networks extracted from an administrative claim dataset. What are the implications for the practitioners? Healthcare managers or administrators can use the framework proposed in the present study to evaluate organisational functioning in terms of effective collaboration and coordination of care in their respective healthcare organisations.


Srinivasan U.,Cooperative Capital
IT Professional | Year: 2014

Using several practical examples of cost and quality-of-care outliers, the author presents a framework to detect outliers and anomalies in healthcare services. © 1999-2012 IEEE.


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